Digital Engineering forms the basis for all three of the SERC crosscutting missions and resulting research roadmaps. We are leading a systems engineering transformation process that is based on the use of data (an Authoritative Source of Truth) and collaboration using models (Collaborative Integrated Modeling Environments). The Digital Engineering research roadmap aligns with the five goals of our DoD sponsor’s strategy: (1) Model Use for Decision Making; (2) the Authoritative Source of Truth (AST); (3) Technological Innovation; (4) Collaborative Environments; and (5) Workforce and Cultural evolution. The progression in Digital Engineering is expected to begin with data integration in the AST followed by the semantic integration of models. We expect to soon see advances in Augmented Intelligence – the use of models and “big data”, that bring automation to engineering processes and system quality and certification. In our Digital Engineering roadmap you see growing maturity through the many research activities underway (yellow items on the roadmap progression).

Hover over nodes to learn more about each research activity.

Velocity and agility are critical characteristics of future systems, both for the system that is being deployed and the system that is developing and maintaining the deployed system. With the fusion of development in operations, DevOps, the delineation between these is disappearing. A research roadmap for Velocity is perhaps the most difficult to articulate as it is rooted in current organizational implementation of these practices and methodologies.

One might ask, where is the needed research? With our defense and other government sponsors, velocity centers on three goals:

1. architecting systems for continuous development and deployment
2. leading an agile transition across large government and contractor systems
3. the role of Collaborative Integrated Modeling Environments as an enabler

Hover over nodes to learn more about each research activity.

The SERC Security roadmap focuses on critical engineered systems such as cyber-physical systems, embedded systems, and weapon systems. These are often highly assured systems. The roadmap recognizes attributes such as security and resilience as critical system properties, and assurance as a process that yields an evidentiary case that a system is trustworthy with respect to the properties its stakeholders legitimately rely upon. Ongoing SERC security research focuses on three areas:

1. prevent, detect, and mitigate security vulnerabilities;
2. design, model, and conduct analysis of trustworthiness (i.e., safe and secure aspects) of complex cyber-physical system capabilities and behaviors;
3. develop models, processes, and tools to assure the trustworthiness of system behaviors/ performance envelopes increasingly driven by machine learning, autonomous capabilities, and manned-unmanned teaming

Research is underway in four areas: Integrated Assurance Processes, which address the system design space in a way that integrates security/safety/reliability and advances practices across all three disciplines; Requirements and Functional Simulation, which focuses on early stage design practices and security patterns (build the right system); Formal Methods and Test, which hopes to advance research in proof driven validation and evidence (build the system right); and Cyber Physical Systems Education, addressing the current shortfall of security related education in engineering programs.

Hover over nodes to learn more about each research activity.

The goal of SERC research in artificial intelligence and autonomy is to lead transformation of systems engineering to dynamic processes that leverage the speed and rigor of rapidly evolving modeling, simulation and analysis computational technologies enabled by computational intelligence. The technical domain of artificial intelligence, machine learning, and autonomy encompasses a broad range of methods, processes, tools, and technologies that are still emerging. In this area we cannot yet define a concrete roadmap linking this technical domain to systems engineering – but we can categorize research areas in an evolutionary framework that we expect to be transformative to the engineering domain. In this space, the “Double S” curve of technological innovation provides an effective categorization of systems engineering research contributions to emerging technology as well as their application. These include abstraction and high-level design methods, design for “X”, and design for test and certification, which lead to ability to specify the technology into requirements, tools to accelerate and scale design, modeling and simulation at the mission level, and finally to operational test and incorporation.

The vectors of this notional roadmap span five categories:

1. AI/ML Technology Evolution vector recognizes that the technological implementation of AI systems will evolve and will need to evolve in directions relevant to SE. Most of these can be related to the development of transparency and trust in technology.
2. Automation & Human-Machine Teaming vector recognizes that the purpose of AI in systems is generally to provide automation of human tasks and decisions.
3. Augmented Engineering vector recognizes that AI technologies will gradually be used more and more to augment the work of engineering.
4. Digital Engineering vector recognizes that the current digital engineering transformation will be an enabler for augmented engineering. 10. Workforce and Culture vector recognizes a significant transformation will need to be accomplished in the SE workforce, with significantly greater integration of software and human behavioral sciences at the forefront.
5. Workforce and Culture vector recognizes a significant transformation will need to be accomplished in the SE workforce, with significantly greater integration of software and human behavioral sciences at the forefront.